How to become an AI developer: beginner’s guide

11 min read
October 30, 2023

The AI market is worth $207.9 billion in 2023 and is set to reach $1.85 trillion by 2030.

So becoming an AI developer is an obvious choice for you.

But, you need to know how to do it right if you want to make the most of it.

In our beginner’s guide, we’ll discuss how you can learn AI development, along with actionable tips and great tools to help you.

Let’s dive in!

Learn the basics of AI development

If you want to learn AI development, you need to have basic knowledge about AI and app development.

After all, it’s easier to build a house if you already have a strong foundation.

But, which basics exactly do you need to get started on your journey as an AI developer?

Of course, you need to have at least some software engineering knowledge and experience – especially app development.

And for AI development, knowing Python is crucial.

Python is the most popular programming language for machine learning and AI.

Python and machine learning

source: BoTree Technologies

A large number of machine learning libraries and AI frameworks are written in Python, so knowing your way around it is essential.

But, you don’t just need programming knowledge.

You should also be familiar with:

  • Linear algebra
  • Calculus
  • Probability
  • Algorithms and data structures
  • Statistics

These disciplines are foundational for machine learning and AI models.

And if you’re familiar with them, you’ll have an easier time understanding how AI models work.

Now, let’s go over why knowing the basics of AI and app development is important when learning AI development.

Why knowing the basics of AI development is important

The main reason why knowing the basics is important is because it will help you get to grips with AI development faster.

You’ll be building on your prior knowledge instead of having to learn everything from scratch.

This will help you create a more focused learning roadmap.

For example, if you already know the difference between machine learning and deep learning, you’ll be able to skip beginner’s AI courses.

AI vs machine learning vs deep learning

source: Singapore Computer Society

This means you’ll be able to move on to more advanced AI topics quicker.

And you’ll save time and money by doing that.

Take AI courses and visit AI forums

Taking courses and visiting specialized forums is a great way to learn the ins and outs of AI development.

They’ll help get to grips with the basics you need to develop AI models and keep you updated about new developments in the world of AI.

And that will help you become an AI engineer.

So, where can you take relevant AI courses and certifications?

There are a number of introductory AI courses available on the market, such as:

And these are just 3 of the best-rated courses and certifications.

You can find many more on sites like Coursera and Udacity.

Udacity UI

source: BitDegree

Pick the ones that are most relevant to your experience and skill level.

But, AI courses are just the start.

You should also visit specialized AI forums like Kaggle and AI Stack Exchange.

There, you’ll find answers to any questions you might have and stay updated on AI news.

Also, make sure to keep an eye out for any AI conferences and meetups in your area.

They’re a great learning and networking opportunity.

Now, let’s talk about why taking AI courses and visiting AI forums is important for learning AI development.

Why taking AI courses and visiting AI forums is important

The main reason why AI courses and forums are important for learning is that they’re the best way to learn how to develop with AI.

AI courses will give you the basic knowledge you need and AI forums will help you with more specific and niche topics.

Also, they’re important because AI is a rapidly evolving field.

AI courses and communities will help you make sense of any new developments.

They’re a great way you can continuously learn and improve your AI development skills.

Continuous learning benefits

source: SelectHub

And they’re also a great way to get hands-on experience.

Many of the courses we’ve mentioned offer a mix of theoretical and practical assignments.

As for AI forums, members often post their projects and search for collaborators.

And that’s another great way to get practical experience with AI development.

Learn to use AI tools and platforms

If you want to become an AI developer, you need to learn how to use AI tools.

And there are a lot of tools and platforms out there you can use.

Which one you go with will depend on the type of AI app you’d like to work on in the future.

So, which tools and platforms should you start with?

The most beginner-friendly platforms you can use are cloud-based AI platforms like:

They’re end-to-end platforms, which means you can go from training to deploying an AI model in an app without leaving the platform.

But, if you want to try to develop and train an AI model on your own, you’ll be using AI frameworks like:

You can also find a wide range of tools on their websites that will help you get started with AI model training.

Now, let’s go over why choosing the right AI tools and platforms is important for learning AI development.

Why choosing the right AI tools and platforms is important

Choosing the right AI tools and platforms is a fundamental step when learning AI development.

The right platform will make it a breeze.

Luckily, most of the AI tools and platforms we’ve mentioned offer detailed tutorials and have extensive documentation that’s publicly available.

These will help you build a solid foundation before you jump in and start developing AI models.

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For example, cloud-based AI platforms like AWS AI offer a range of courses that will help you master the basics of AI development.

Also, these tools and platforms often have very active community forums.

The PyTorch and TensorFlow forums in particular are active and you can find additional resources and tutorials there.

They’ll also be able to help you with troubleshooting if you run into problems when developing an AI app.

And that’s why making the right choice is key.

Learn about data collection and preprocessing

Data is the foundation of AI.

And high-quality data is key to the success of any AI model you’ll develop.

That’s why learning about data collection and preprocessing is crucial.

So, how can you collect data for your AI model?

One common way to collect data is using crowdsourcing platforms.

Another common method is web scraping.

But, if you’re just starting out, they’re not really convenient options.

Luckily, there are plenty of publicly available datasets on sites like:

You can freely use the datasets you find to train or fine-tune your AI model.

But, just finding and using those datasets isn’t enough.

You also have to preprocess the data to get the best results.

Data preprocessing

source: Serokell

Data preprocessing is a key step when developing AI models because it increases the quality of your data.

But, it can be challenging even for experienced AI engineers.

That’s why you should get familiar with it, on a basic level, when you’re starting out.

You’ll be setting yourself up for success if you do.

Now, let’s talk about why learning about data collection and preprocessing is so important.

Why learning about data collection and preprocessing is important

One key reason these two concepts are important for AI development is that they directly affect your AI model’s outputs.

Even the best designed AI model won’t work if it’s trained on bad data.

This is the classic “garbage in, garbage out” principle in action.

Knowing how (and which) data to collect and how to preprocess it to increase its quality is crucial if you want your AI model to work properly.

And poor data management can have serious consequences.

Remember Microsoft’s chatbot, Tay?

Tay chatbot

source: TechRepublic

Tay was launched in 2016 and was supposed to learn from user interactions.

But, soon after launch, Tay began sending inappropriate tweets and was promptly shut down.

This is an example of why high-quality data is so important for AI models.

And if you learn how to collect and preprocess it, you’ll become a better AI engineer.

That’s because you’ll know how to effectively train your model and test its outputs.

And that will help you build and train better AI models.

Start with basic AI models and algorithms

Would you start building a house’s roof before laying its foundation?

Of course you wouldn’t.

And you can’t become an AI developer without first learning how to build basic AI models, either.

They’re foundational to complex AI models and you need to learn how to work with them before building advanced AI models.

So, what do you need to know to start building basic AI models and algorithms?

First, you need to know the basic model training approaches:

  • Supervised learning
  • Unsupervised learning
  • Reinforcement learning

Supervised learning models are trained on labeled datasets and the models have access to the correct answers.

Unsupervised learning models, on the other hand, are trained on unlabeled data and learn on their own.

And reinforcement learning models are trained by rewarding desired model behaviors and punishing undesired ones.

Here’s an overview of some of the most common models and algorithms:

Machine learning algorithms

source: Analytics Vidhya

Of course, you don’t need to learn how to build and use every single one of these models and algorithms.

But, learning the most common ones like decision trees and linear regression will help you build a solid understanding of how AI models work.

Now, let’s discuss in more detail why starting with the basics is important.

Why starting with basic AI models and algorithms is important

We’ve already mentioned that basic AI models and algorithms are foundational to complex AI models.

And that’s so important that we need to talk about it in more detail.

That’s because they’re not just foundational to advanced AI models, they’re also foundational to your understanding of AI models in general.

Once you learn how to work with basic models and algorithms, you’ll have a much easier time understanding how advanced models work.


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You’ll be talking with our technology experts.

You’ll get a deeper understanding of the underlying principles of how AI models work.

And that’s essential if you want to become an AI developer.

Also, you can train and test basic models and algorithms faster than complex ones.

This makes experimentation and iteration easier and faster.

That will help you quickly get hands-on experience with developing AI models.

And that’s going to make all the difference.

Follow AI development best practices

Getting started with AI development is a great way to take your engineering skills to the next level.

But, you need to do it right.

That’s why you should follow AI development best practices so you can get better at it:

  • Use high-quality data – use high-quality data to ensure your AI model’s outputs are consistent and accurate
  • Start with basic AI models – master the basics before moving on to more complex AI models
  • Test and validate your model’s outputs – this way, you’ll ensure your AI model is working properly
  • Stay updated – as AI is a constantly evolving field, make sure you keep up with new developments

If you keep these in mind, you’ll be able to build better AI models in the future.

And in the end, that’s what matters the most.

How to become an AI developer: conclusion 

AI development is the next big thing.

But, before you can start building your own AI models, you have to learn how to do it right.

And that’s why we’ve written this beginner’s guide to AI development.

To recap, the steps you need to take to learn it are:

  • Learn the basics of AI development
  • Take AI courses and visit AI forums
  • Learn to use AI tools and platforms
  • Learn about data collection and preprocessing
  • Start with basic AI models and algorithms
  • Follow AI development best practices

If you want to learn more, check out how we build AI apps and read our step-by-step guide on how to develop an AI app.

Written by

Igor Caleta-Car

Solution Architect

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